Muestra métricas de impacto externas asociadas a la publicación. Para mayor detalle:
| Indexado |
|
||||
| DOI | 10.1109/ACCESS.2023.3292887 | ||||
| Año | 2023 | ||||
| Tipo | artículo de investigación |
Citas Totales
Autores Afiliación Chile
Instituciones Chile
% Participación
Internacional
Autores
Afiliación Extranjera
Instituciones
Extranjeras
Mobile code offloading is a well-known technique for enhancing the capabilities of mobile platforms by transparently leveraging the resources to the cloud. Although this technique has been studied for years, little empirical evidence exists to demonstrate its alleged benefits in terms of performance in real-life situations. All studies conducted on this topic have so far been relegated to controlled environments in laboratory settings. As such, there is no evidence of how and how well this technique performs in real-life scenarios, where network unreliability is the norm. In this work, we present the first empirical study of an Android mobile application integrated with a code offloading framework being tested in the wild. We distributed an application that contains a set of benchmarks in APK format and deployed it on a wide gamut of Android devices to which we had no physical access. We carefully detail the methodology and infrastructure we used to monitor the benchmarks' performance of 18 devices. Overall, our results show that the accuracy of the decision-making engine is heavily affected by a couple of factors, mainly the network diagnosis and connection type. Therefore, determining whether or not it is more convenient to execute a given task in the cloud is a difficult task. We summarize five lessons we learned by performing our experiment that we believe should be considered for future experiments in this area.
| Ord. | Autor | Género | Institución - País |
|---|---|---|---|
| 1 | Sanabria, Pablo | Hombre |
Pontificia Universidad Católica de Chile - Chile
Centro Nacional de Inteligencia Artificial (CENIA) - Chile Centro Nacional de Inteligencia Artificial - Chile |
| 2 | Neyem, Andres | Hombre |
Pontificia Universidad Católica de Chile - Chile
Centro Nacional de Inteligencia Artificial (CENIA) - Chile Centro Nacional de Inteligencia Artificial - Chile |
| 3 | Alcocer, Juan Pablo Sandoval | Hombre |
Pontificia Universidad Católica de Chile - Chile
|
| 4 | Fernandez-Blanco, Alison | Mujer |
Pontificia Universidad Católica de Chile - Chile
|
| Fuente |
|---|
| National Agency for Research and Development (ANID)/Scholarship Program/DOCTORADO NACIONAL |
| ANID FONDECYT Iniciacion Folio |
| National Center for Artificial Intelligence (CENIA), Basal ANID |
| Agradecimiento |
|---|
| This work was supported in part by the National Agency for Research and Development (ANID)/Scholarship Program/DOCTORADO NACIONAL under Grant 2020-21200979; and in part by the National Center for Artificial Intelligence (CENIA), Basal ANID, under Grant FB210017. The work of Sandoval Alcocer Juan Pablo was supported by ANID FONDECYT Iniciacion Folio under Grant 11220885. |